Bootstrap Cliques of Transition Networks from Sequence Data

Bootstrap the edge weights of all cliques of a given size in a tna model, producing per-clique mean weights, p-values, confidence intervals, and consistency-range bounds.

Usage

bootstrap_cliques(
  x,
  size,
  threshold,
  iter,
  level,
  consistency_range,
  seed = NULL
)

# S3 method for class 'tna'
bootstrap_cliques(
  x,
  size = 2L,
  threshold = 0,
  iter = 1000,
  level = 0.05,
  consistency_range = c(0.75, 1.25),
  seed = NULL
)

Arguments

x: A tna or a group_tna object.
size: An integer specifying the size of the cliques to identify. Defaults to 2 (dyads).
threshold: A numeric value that sets the minimum edge weight for an edge to be considered in the clique. Edges below this value are ignored. Defaults to 0.
iter: An integer specifying the number of bootstrap samples to draw. Defaults to 1000.
level: A numeric value representing the significance level for hypothesis testing and confidence intervals. Defaults to 0.05.
consistency_range: A numeric vector of length 2. Determines how much the edge weights may deviate (multiplicatively) from their observed values (below and above) before they are considered insignificant. The default is c(0.75, 1.25) which corresponds to a symmetric 25% deviation range. Used only when method = "stability".
seed: A single numeric random seed for reproducible resampling, or NULL (the default) to use the current RNG state.

Value

A data.frame (also of class tna_bootstrap_cliques) with one row per clique and the columns clique, mean_weight, p_values, sig, cr_lower, cr_upper, ci_lower, ci_upper.

Examples

model <- tna(group_regulation)
# Small number of iterations for CRAN
boot_cliq <- bootstrap_cliques(model, size = 2, iter = 10)